Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

Title

Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

Description

This is an Accepted Manuscript of an article published by IEEE. Schrum, J., & Miikkulainen, R. (2016). Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks. IEEE Transactions on Computational Intelligence and AI in Games, 8(1), 67–81. http://doi.org/10.1109/TCIAIG.2015.2390615. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")

Creator

Schrum, Jacob

Miikkulainen, Risto

Date

2016-12-13

Date Available

2016-12-13

Date Issued

2016

Identifier

Schrum, J., & Miikkulainen, R. (2016). Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks. IEEE Transactions on Computational Intelligence and AI in Games, 8(1), 67–81. http://doi.org/10.1109/TCIAIG.2015.2390615

uri

https://collections.southwestern.edu/s/suscholar/item/233

Abstract

Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games.

Language

English

Publisher

IEEE

Subject

Multiobjective Optimization

Multimodal Behavior

Neuroevolution

Ms. Pac-Man

Modularity

Type

Article